python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "NP.csv" --strategy-args '{"horizon": 24, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 24, "lr": 0.0001, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 168}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "NP/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "NP.csv" --strategy-args '{"horizon": 360, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 360, "lr": 0.0001, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 720}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "NP/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "PJM.csv" --strategy-args '{"horizon": 24, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 24, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 168}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "PJM/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "PJM.csv" --strategy-args '{"horizon": 360, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 360, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 720}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "PJM/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "BE.csv" --strategy-args '{"horizon": 24, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 24, "lr": 0.001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 168}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "BE/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "BE.csv" --strategy-args '{"horizon": 360, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 360, "lr": 0.001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 720}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "BE/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "FR.csv" --strategy-args '{"horizon": 24, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 24, "lr": 0.0001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 168}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "FR/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "FR.csv" --strategy-args '{"horizon": 360, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 360, "lr": 0.0001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 720}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "FR/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "DE.csv" --strategy-args '{"horizon": 24, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 32, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 24, "lr": 0.003, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 168}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "DE/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "DE.csv" --strategy-args '{"horizon": 360, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 32, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 360, "lr": 0.003, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 720}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "DE/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Energy.csv" --strategy-args '{"horizon": 24, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 1, "fusion_method": "mlp", "horizon": 24, "lr": 0.003, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 168}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Energy/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Energy.csv" --strategy-args '{"horizon": 360, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 1, "fusion_method": "mlp", "horizon": 360, "lr": 0.003, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 720}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Energy/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Sdwpfm1.csv" --strategy-args '{"horizon": 24, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 24, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 168}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Sdwpfm1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Sdwpfm1.csv" --strategy-args '{"horizon": 360, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 360, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 720}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Sdwpfm1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Sdwpfm2.csv" --strategy-args '{"horizon": 24, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 24, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 168}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Sdwpfm2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Sdwpfm2.csv" --strategy-args '{"horizon": 360, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 360, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 720}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Sdwpfm2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Sdwpfh1.csv" --strategy-args '{"horizon": 24, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 24, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 168}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Sdwpfh1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Sdwpfh1.csv" --strategy-args '{"horizon": 360, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 360, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 720}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Sdwpfh1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Sdwpfh2.csv" --strategy-args '{"horizon": 24, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 24, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 168}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Sdwpfh2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Sdwpfh2.csv" --strategy-args '{"horizon": 360, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 360, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 720}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Sdwpfh2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Colbun.csv" --strategy-args '{"horizon": 10, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 10, "lr": 0.01, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 60}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Colbun/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Colbun.csv" --strategy-args '{"horizon": 30, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 30, "lr": 0.01, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 180}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Colbun/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Rapel.csv" --strategy-args '{"horizon": 10, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 64, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 10, "lr": 0.01, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 60}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Rapel/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Rapel.csv" --strategy-args '{"horizon": 30, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 64, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 30, "lr": 0.01, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 180}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Rapel/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTh1.csv" --strategy-args '{"horizon": 96, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 96, "lr": 0.001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTh1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTh1.csv" --strategy-args '{"horizon": 192, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 192, "lr": 0.001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTh1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTh1.csv" --strategy-args '{"horizon": 336, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 336, "lr": 0.001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTh1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTh1.csv" --strategy-args '{"horizon": 720, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 720, "lr": 0.001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTh1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTh2.csv" --strategy-args '{"horizon": 96, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 96, "lr": 0.0005, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTh2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTh2.csv" --strategy-args '{"horizon": 192, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 192, "lr": 0.0005, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTh2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTh2.csv" --strategy-args '{"horizon": 336, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 336, "lr": 0.0005, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTh2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTh2.csv" --strategy-args '{"horizon": 720, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 720, "lr": 0.0005, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTh2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTm1.csv" --strategy-args '{"horizon": 96, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 96, "lr": 0.0001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTm1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTm1.csv" --strategy-args '{"horizon": 192, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 192, "lr": 0.0001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTm1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTm1.csv" --strategy-args '{"horizon": 336, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 336, "lr": 0.0001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTm1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTm1.csv" --strategy-args '{"horizon": 720, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 720, "lr": 0.0001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTm1/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTm2.csv" --strategy-args '{"horizon": 96, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 1, "fusion_method": "mlp", "horizon": 96, "lr": 0.0001, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTm2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTm2.csv" --strategy-args '{"horizon": 192, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 1, "fusion_method": "mlp", "horizon": 192, "lr": 0.0001, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTm2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTm2.csv" --strategy-args '{"horizon": 336, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 1, "fusion_method": "mlp", "horizon": 336, "lr": 0.0001, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTm2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "ETTm2.csv" --strategy-args '{"horizon": 720, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 1, "fusion_method": "mlp", "horizon": 720, "lr": 0.0001, "mlp_hidden_dims": 256, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "ETTm2/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Weather.csv" --strategy-args '{"horizon": 96, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 96, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Weather/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Weather.csv" --strategy-args '{"horizon": 192, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 192, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Weather/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Weather.csv" --strategy-args '{"horizon": 336, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 336, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Weather/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Weather.csv" --strategy-args '{"horizon": 720, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 720, "lr": 0.0001, "mlp_hidden_dims": 32, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Weather/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Electricity.csv" --strategy-args '{"horizon": 96, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 96, "lr": 0.0005, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Electricity/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Electricity.csv" --strategy-args '{"horizon": 192, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 192, "lr": 0.0005, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Electricity/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Electricity.csv" --strategy-args '{"horizon": 336, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 336, "lr": 0.0005, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Electricity/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Electricity.csv" --strategy-args '{"horizon": 720, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 720, "lr": 0.0005, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Electricity/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Traffic.csv" --strategy-args '{"horizon": 96, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 96, "lr": 0.01, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Traffic/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Traffic.csv" --strategy-args '{"horizon": 192, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 192, "lr": 0.01, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Traffic/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Traffic.csv" --strategy-args '{"horizon": 336, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 336, "lr": 0.01, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Traffic/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Traffic.csv" --strategy-args '{"horizon": 720, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 32, "begin_order": 0, "d_ff": 32, "d_model": 16, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 720, "lr": 0.01, "norm": true, "num_epochs": 10, "patience": 10, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Traffic/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Exchange.csv" --strategy-args '{"horizon": 96, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 96, "lr": 0.0001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Exchange/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Exchange.csv" --strategy-args '{"horizon": 192, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 192, "lr": 0.0001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Exchange/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Exchange.csv" --strategy-args '{"horizon": 336, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 336, "lr": 0.0001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Exchange/TimeKAN"

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "Exchange.csv" --strategy-args '{"horizon": 720, "target_channel": [-1]}' --model-name "timekan.TimeKAN" --model-hyper-params '{"batch_size": 64, "begin_order": 0, "d_ff": 16, "d_model": 32, "down_sampling_layer": 1, "down_sampling_window": 2, "e_layers": 2, "fusion_method": "mlp", "horizon": 720, "lr": 0.0001, "mlp_hidden_dims": 128, "norm": true, "num_epochs": 10, "patience": 3, "seq_len": 96}' --gpus 0 --num-workers 1 --timeout 60000 --save-path "Exchange/TimeKAN"
